A Hybrid Genetic/Optimization Algorithm for Finite-Horizon, Partially Observed Markov Decision Processes

References

• Bean J. C. Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. (1994) 6:154–160Link, Google Scholar
• Bertsekas D. P.Dynamic Programming: Deterministic and Stochastic Models (1987) (Prentice Hall, Englewood Cliffs, NJ) Google Scholar
• Cassandra A. R. Exact and approximate algorithms for partially observable Markov processes. (1998) . Ph.D. thesis, Department of Computer Science, Brown University, Providence, RIGoogle Scholar
• Cassandra A. R., Kaelbling L. P., Littman M. L. Acting optimally in partially observable stochastic domains. (1994) . Technical report CS-94-20, Department of Computer Science, Brown University, Providence, RIGoogle Scholar
• Cassandra A. R., Littman M. L., Zhang N. L. Incremental pruning: A simple, fast, exact algorithm for partially observable Markov decision processes. (1997) . Technical report, Department of Computer Science, Brown University, Providence, RIGoogle Scholar
• Cheng H-T. Algorithms for partially observable Markov decision processes. (1988) . Ph.D. thesis, University of British Columbia, Vancouver, British Columbia, CanadaGoogle Scholar
• Davidor Y. A naturally occurring niche and species phenomenon: The model and first results. Proc. of the Fourth Internat. Conf. Genetic Algorithms (1991) (Morgan Kaufmann, San Francisco, CA) Google Scholar
• De Jong K. A. An analysis of the behavior of a class of genetic adaptive systems. (1975) . Ph.D. thesis, Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MIGoogle Scholar
• Eagle J. N. The optimal search for a moving target when the search path is constrained. Oper. Res. (1984) 32:1107–1115Link, Google Scholar
• Goldberg D. E., Richardson J. J. Genetic algorithms with sharing for multimodal function optimization. Proc. of 2nd Internat. Conf. on Genetic Algorithms (1987) (Lawrence Erlbaum Publishers, Mahwah, NJ) Google Scholar
• Gorges-Schleuter M. Explicit parallelism of genetic algorithms through population structure. Parallel Problem Solving from Nature (1990) (Springer-Verlag, Berlin, Germany) 150–159Google Scholar
• Hadj-Alouane A. B., Bean J. C. A genetic algorithm for the multiple-choice integer program. Oper. Res. (1997) 45:92–101Link, Google Scholar
• Hadj-Alouane A. B., Bean J. C., Murty K. G. A hybrid genetic/optimization algorithm for a task allocation problem. J. Scheduling (1999) 2:189–201Crossref, Google Scholar
• Holland J. H.Adaptation in Natural and Artificial Systems (1975) (The University of Michigan Press, Ann Arbor, MI) Google Scholar
• Holland J. H.Adaptation in Natural and Artificial Systems (1992) (MIT Press, Cambridge, MA) Crossref, Google Scholar
• Lark J. W. A heuristic search approach for solving finite horizon, completely unobserved Markov decision processes. (1989) . Ph.D. thesis, Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VAGoogle Scholar
• Lin Z-Z. A hybrid genetic/optimization algorithm for a class of sequential decision models. (1999) . Ph.D. thesis, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MIGoogle Scholar
• Lin Z-Z., Bean J. C., White C. C. Genetic algorithm heuristics for finite horizon partially observed Markov decision processes. (1998) . Technical report 98–24, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MIGoogle Scholar
• Littman M. L. Algorithms for sequential decision making. (1996) . Ph.D. thesis, Department of Computer Science, Brown University, Providence, RIGoogle Scholar
• Littman M. L., Cassandra A. R., Kaelbling L. P. Efficient dynamic programming updates in partially observable Markov decision processes. (1995) . Techinical report CS-95-19, Department of Computer Science, Brown University, Providence, RIGoogle Scholar
• Lovejoy W. S. A survey of algorithmic methods for the partially observable Markov decision processes. Ann. Oper. Res. (1991) 28:47–66Crossref, Google Scholar
• Mahfoud S. W. Niching method for genetic algorithms. (1995) . Ph.D. thesis, Department of General Engineering, University of Illinois, Urbana-Champaign, ILGoogle Scholar
• Monahan G. E. A survey of partially observable Markov decision processes: Theory, models, and algorithms. Management Sci. (1982) 28:1–16Link, Google Scholar
• Norman B. A., Bean J. C. A random keys genetic algorithm for job shop scheduling. Engrg. Design Automation (1997) 3:145–156Google Scholar
• Norman B. A., Bean J. C. A genetic algorithm methodology for complex scheduling problems. Naval Research Logistics (1999) 46:199–211Crossref, Google Scholar
• Norman B. A., Bean J. C. Operation scheduling for parallel machine tools. IIE Transactions (2000) 32:449–459Crossref, Google Scholar
• Puterman M. L.Markov Decision Processes (1994) (John Wiley and Sons, Inc., New York) Crossref, Google Scholar
• Smallwood R. D., Sondik E. J. The optimal control of partially observable Markov decision processes over a finite horizon. Oper. Res. (1973) 21:1071–1088Link, Google Scholar
• Sondik E. J. The optimal control of partially observable Markov processes. (1971) . Ph.D. thesis, Department of Electrical Engineering, Stanford University, Palo Alto, CAGoogle Scholar
• White D. J. Real applications of Markov decision processes. Interfaces (1985) 15:7–83Link, Google Scholar
• White D. J. Further real applications of Markov decision processes. Interfaces (1988) 18:55–61Link, Google Scholar
• White D. J. Piecewise linear approximation for partially observable Markov decision processes with finite horizons. J. Inform. Optim. Sci. (1992) 13:311–324Crossref, Google Scholar
• White III., C C. A survey of solution techniques for the partially observed Markov decision processes. Ann. Oper. Res. (1991) 32:215–230Crossref, Google Scholar
• White III., C C., White D. J. Markov decision processes. Eur. J. Oper. Res. (1989) 39:1–16Crossref, Google Scholar
• White III., C C., Wilson E. C., Weaver A. C. Decision aid development for use in ambulatory health care settings. Oper. Res. (1982) 30:446–463Link, Google Scholar
• Zhang N. L., Liu W. Planning in stochastic domains: problem characteristics and approximation. (1996) . Technical report HKUST-CS96-31, Department of Computer Science, Hong Kong University of Science and Technology, Hong KongGoogle Scholar